9 datasets found
  1. k

    BBC Stock: A High-Growth Company with a Bright Future (Forecast)

    • kappasignal.com
    Updated Oct 15, 2023
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    KappaSignal (2023). BBC Stock: A High-Growth Company with a Bright Future (Forecast) [Dataset]. https://www.kappasignal.com/2023/10/bbc-stock-high-growth-company-with.html
    Explore at:
    Dataset updated
    Oct 15, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    BBC Stock: A High-Growth Company with a Bright Future

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  2. N

    News Apps Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Apr 24, 2025
    + more versions
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    Data Insights Market (2025). News Apps Report [Dataset]. https://www.datainsightsmarket.com/reports/news-apps-1407722
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global news app market is experiencing robust growth, driven by increasing smartphone penetration, readily available high-speed internet, and a rising demand for personalized and on-the-go news consumption. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. Key growth drivers include the increasing adoption of subscription models by news publishers, offering premium content and ad-free experiences, as well as sophisticated algorithms providing personalized news feeds. Furthermore, advancements in artificial intelligence (AI) are enabling enhanced features like real-time news alerts, curated content based on user preferences, and advanced search functionalities. The market is segmented by application (subscription services and advertising) and by type (Android, iOS, web app, and others), with significant variations in revenue generation and user base across these categories. Competition is intense, with established tech giants like Apple, Google, and Microsoft vying for market share alongside specialized news providers such as The New York Times, BBC, and CNN, and emerging players leveraging social media integration and innovative content formats. Geographic distribution shows North America and Europe currently dominating the market, but significant growth potential lies within the Asia-Pacific region, driven primarily by the expanding digital landscape and increasing internet penetration in countries like India and China. Market restraints include concerns regarding data privacy and security, the spread of misinformation and “fake news,” and the evolving challenges of maintaining a sustainable revenue model in a fiercely competitive environment. The industry faces ongoing challenges in monetizing user engagement and balancing user experience with data collection practices. Future growth will likely depend on the ability of news apps to adapt to evolving user preferences, enhance their features through AI and machine learning, effectively address misinformation, and successfully navigate the complexities of data privacy regulations. The ongoing development of personalized news experiences, coupled with innovative subscription models and strategic partnerships, will be crucial for sustained success in this dynamic market.

  3. bbc.com Website Traffic, Ranking, Analytics [October 2025]

    • semrush.ebundletools.com
    Updated Nov 12, 2025
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    Semrush (2025). bbc.com Website Traffic, Ranking, Analytics [October 2025] [Dataset]. https://semrush.ebundletools.com/website/bbc.com/overview/
    Explore at:
    Dataset updated
    Nov 12, 2025
    Dataset authored and provided by
    Semrushhttps://fr.semrush.com/
    License

    https://semrush.ebundletools.com/company/legal/terms-of-service/https://semrush.ebundletools.com/company/legal/terms-of-service/

    Time period covered
    Nov 12, 2025
    Area covered
    Worldwide
    Variables measured
    visits, backlinks, bounceRate, pagesPerVisit, authorityScore, organicKeywords, avgVisitDuration, referringDomains, trafficByCountry, paidSearchTraffic, and 3 more
    Measurement technique
    Semrush Traffic Analytics; Click-stream data
    Description

    bbc.com is ranked #72 in US with 442.47M Traffic. Categories: Newspapers. Learn more about website traffic, market share, and more!

  4. T

    Television Broadcasting Service Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Jun 24, 2025
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    Archive Market Research (2025). Television Broadcasting Service Report [Dataset]. https://www.archivemarketresearch.com/reports/television-broadcasting-service-358737
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global Television Broadcasting Service market is booming, reaching an estimated $750 billion in 2025 and projected to grow at a 7% CAGR through 2033. Discover key trends, challenges, and leading players shaping this dynamic industry, including streaming, 4K, and digital broadcasting.

  5. bbc-doctorwho.ru Website Traffic, Ranking, Analytics [October 2025]

    • semrush.ebundletools.com
    Updated Nov 12, 2025
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    Semrush (2025). bbc-doctorwho.ru Website Traffic, Ranking, Analytics [October 2025] [Dataset]. https://semrush.ebundletools.com/website/bbc-doctorwho.ru/overview/
    Explore at:
    Dataset updated
    Nov 12, 2025
    Dataset authored and provided by
    Semrushhttps://fr.semrush.com/
    License

    https://semrush.ebundletools.com/company/legal/terms-of-service/https://semrush.ebundletools.com/company/legal/terms-of-service/

    Time period covered
    Nov 12, 2025
    Area covered
    Worldwide
    Variables measured
    visits, backlinks, bounceRate, pagesPerVisit, authorityScore, organicKeywords, avgVisitDuration, referringDomains, trafficByCountry, paidSearchTraffic, and 3 more
    Measurement technique
    Semrush Traffic Analytics; Click-stream data
    Description

    bbc-doctorwho.ru is ranked #1075 in RU with 1.62M Traffic. Categories: . Learn more about website traffic, market share, and more!

  6. P

    Pressure Wave Switch Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Aug 11, 2025
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    Archive Market Research (2025). Pressure Wave Switch Report [Dataset]. https://www.archivemarketresearch.com/reports/pressure-wave-switch-804202
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Aug 11, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global pressure wave switch market is experiencing robust growth, projected to reach a market size of $500 million by 2025, expanding at a Compound Annual Growth Rate (CAGR) of 7%. This growth is fueled by several key drivers, including the increasing demand for advanced pressure sensing technologies across diverse industries. Automation in manufacturing, particularly in process industries like chemicals and pharmaceuticals, necessitates precise pressure monitoring and control, driving the adoption of pressure wave switches. Furthermore, the growing emphasis on safety and efficiency in industrial applications contributes significantly to market expansion. The automotive sector, with its rising adoption of advanced driver-assistance systems (ADAS) and electric vehicles (EVs), also presents a lucrative opportunity for pressure wave switch manufacturers. While the market faces challenges like the high initial investment cost for advanced systems and potential supply chain disruptions, the overall trend points towards sustained and significant growth over the next decade. The market is segmented by type (e.g., mechanical, electronic), application (e.g., automotive, industrial automation, medical), and geography. Key players such as BBC Bircher, Albin Pump, Parker US, and CEJN AB are vying for market share through product innovation and strategic partnerships. Regional variations exist, with North America and Europe currently dominating the market due to established industrial infrastructure and a higher adoption rate of advanced technologies. However, developing economies in Asia-Pacific and other regions are expected to witness substantial growth in the coming years, propelled by industrialization and infrastructural development. This expansion is expected to continue into the forecast period (2025-2033), with a continued increase in demand and further technological advancements expected to shape the future of the pressure wave switch market.

  7. m

    Data for: Nuclear hazard and asset prices: Implications of nuclear disasters...

    • data.mendeley.com
    Updated Nov 16, 2020
    + more versions
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    Ana Belén Alonso-Conde (2020). Data for: Nuclear hazard and asset prices: Implications of nuclear disasters in the cross-sectional behavior of stock returns [Dataset]. http://doi.org/10.17632/wv94fj59t4.3
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    Dataset updated
    Nov 16, 2020
    Authors
    Ana Belén Alonso-Conde
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    Using all stocks listed on the Japanese equity market and macroeconomic data for Japan, the dataset comprises the following series:

    1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
    4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
    6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
    7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
    8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
    9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
    10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

    We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

    REFERENCES:

    Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

  8. t

    television broadcasting service Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jun 9, 2025
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    Data Insights Market (2025). television broadcasting service Report [Dataset]. https://www.datainsightsmarket.com/reports/television-broadcasting-service-470595
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Jun 9, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global television broadcasting service market is a dynamic and competitive landscape, exhibiting significant growth potential. While precise figures for market size and CAGR are unavailable, we can infer substantial growth based on industry trends and the involvement of major players like CBS Interactive, CANAL+ GROUP, BBC, AT&T, and A&E Television Networks. The market's expansion is fueled by several key drivers: increasing internet and mobile penetration, the rise of streaming services and on-demand content, and the growing adoption of advanced technologies like High Definition (HD) and Ultra High Definition (UHD) broadcasting. These trends are transforming consumer viewing habits, leading to a shift from traditional linear television towards a more diverse and personalized viewing experience. The market is segmented based on various factors like content type (news, sports, entertainment), distribution channels (cable, satellite, IPTV, streaming), and geographic regions. The competitive landscape is characterized by both established players and emerging new entrants, each vying for market share through innovative content strategies, technological advancements, and strategic partnerships. Despite challenges such as increasing production costs and regulatory hurdles, the market's long-term prospects remain strong, driven by ongoing technological innovation and evolving consumer preferences. The forecast period from 2025 to 2033 presents several opportunities for growth. The continued penetration of streaming platforms and the growth of smart TVs are expected to drive significant increases in viewership and revenue. Companies are strategically investing in original content and enhancing user experience through personalized recommendations and interactive features. Competition will remain fierce, necessitating a focus on differentiation through high-quality programming, unique content strategies, and advanced technological capabilities. Regional variations will also be notable, with differing adoption rates of new technologies and content preferences impacting market growth. Successful players will need to adapt quickly to changing consumer behavior and effectively leverage technological advancements to maintain a competitive edge in this rapidly evolving market.

  9. Time spent with media in the UK 2018-2024, by format

    • statista.com
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    Statista, Time spent with media in the UK 2018-2024, by format [Dataset]. https://www.statista.com/statistics/1230381/time-spent-media-by-type-united-kingdom/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United Kingdom
    Description

    The shift in media consumption habits in the United Kingdom is evident, with digital platforms gaining ground over traditional formats. By 2026, consumers in the country are projected to spend over *** hours daily on digital media, while traditional media usage is expected to decline to just over ***** hours. This trend reflects a broader transformation in how people engage with content and information in the digital age. Digital dominance reshapes the entertainment landscape The entertainment and media market in the UK is poised for significant growth, with projections indicating a ****-percent compound annual growth rate between 2024 and 2028, potentially reaching *** billion British pounds. This expansion is driven by changing consumer preferences, particularly in digital media consumption. This is evident, when it comes to video viewing time – traditional sources such as broadcaster TV are losing popularity in favor of YouTube and VOD. And so, digital platforms continue to lead the way in capturing audience attention and market share. Video at the helm of consumers'entertainment time Social media and video streaming platforms are neck-and-neck in capturing user engagement in the UK. As of May 2023, both TikTok and Netflix users spent an average of ** minutes daily on their respective platforms. This parity highlights the presence of many forms of digital video in the daily lives of UK consumers. However, while subscription-based video services maintain a strong presence, there's a growing trend towards free, ad-supported options. Broadcaster video-on-demand (BVOD) providers, particularly BBC iPlayer, are gaining traction among all online TV platforms, indicating a shift in viewer preferences and potentially reshaping the streaming landscape.

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    Learn how you can add new datasets to our index.

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KappaSignal (2023). BBC Stock: A High-Growth Company with a Bright Future (Forecast) [Dataset]. https://www.kappasignal.com/2023/10/bbc-stock-high-growth-company-with.html

BBC Stock: A High-Growth Company with a Bright Future (Forecast)

Explore at:
Dataset updated
Oct 15, 2023
Dataset authored and provided by
KappaSignal
License

https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

Description

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BBC Stock: A High-Growth Company with a Bright Future

Financial data:

  • Historical daily stock prices (open, high, low, close, volume)

  • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

  • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

  • Feature engineering based on financial data and technical indicators

  • Sentiment analysis data from social media and news articles

  • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

  • Stock price prediction

  • Portfolio optimization

  • Algorithmic trading

  • Market sentiment analysis

  • Risk management

Use Cases:

  • Researchers investigating the effectiveness of machine learning in stock market prediction

  • Analysts developing quantitative trading Buy/Sell strategies

  • Individuals interested in building their own stock market prediction models

  • Students learning about machine learning and financial applications

Additional Notes:

  • The dataset may include different levels of granularity (e.g., daily, hourly)

  • Data cleaning and preprocessing are essential before model training

  • Regular updates are recommended to maintain the accuracy and relevance of the data

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